Overview

Dataset statistics

Number of variables15
Number of observations45984
Missing cells161745
Missing cells (%)23.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 MiB
Average record size in memory120.0 B

Variable types

Categorical6
Numeric9

Alerts

country has a high cardinality: 222 distinct values High cardinality
iso_code has a high cardinality: 222 distinct values High cardinality
date has a high cardinality: 294 distinct values High cardinality
vaccines has a high cardinality: 68 distinct values High cardinality
source_name has a high cardinality: 86 distinct values High cardinality
source_website has a high cardinality: 146 distinct values High cardinality
total_vaccinations is highly correlated with people_vaccinated and 3 other fieldsHigh correlation
people_vaccinated is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
people_fully_vaccinated is highly correlated with total_vaccinations and 5 other fieldsHigh correlation
daily_vaccinations_raw is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
daily_vaccinations is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
total_vaccinations_per_hundred is highly correlated with people_fully_vaccinated and 3 other fieldsHigh correlation
people_vaccinated_per_hundred is highly correlated with total_vaccinations_per_hundred and 2 other fieldsHigh correlation
people_fully_vaccinated_per_hundred is highly correlated with people_fully_vaccinated and 3 other fieldsHigh correlation
daily_vaccinations_per_million is highly correlated with total_vaccinations_per_hundred and 2 other fieldsHigh correlation
total_vaccinations is highly correlated with people_vaccinated and 3 other fieldsHigh correlation
people_vaccinated is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
people_fully_vaccinated is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
daily_vaccinations_raw is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
daily_vaccinations is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
total_vaccinations_per_hundred is highly correlated with people_vaccinated_per_hundred and 1 other fieldsHigh correlation
people_vaccinated_per_hundred is highly correlated with total_vaccinations_per_hundred and 1 other fieldsHigh correlation
people_fully_vaccinated_per_hundred is highly correlated with total_vaccinations_per_hundred and 1 other fieldsHigh correlation
total_vaccinations is highly correlated with people_vaccinated and 3 other fieldsHigh correlation
people_vaccinated is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
people_fully_vaccinated is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
daily_vaccinations_raw is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
daily_vaccinations is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
total_vaccinations_per_hundred is highly correlated with people_vaccinated_per_hundred and 2 other fieldsHigh correlation
people_vaccinated_per_hundred is highly correlated with total_vaccinations_per_hundred and 2 other fieldsHigh correlation
people_fully_vaccinated_per_hundred is highly correlated with total_vaccinations_per_hundred and 1 other fieldsHigh correlation
daily_vaccinations_per_million is highly correlated with total_vaccinations_per_hundred and 1 other fieldsHigh correlation
vaccines is highly correlated with source_nameHigh correlation
source_name is highly correlated with vaccinesHigh correlation
total_vaccinations is highly correlated with people_vaccinated and 5 other fieldsHigh correlation
people_vaccinated is highly correlated with total_vaccinations and 5 other fieldsHigh correlation
people_fully_vaccinated is highly correlated with total_vaccinations and 5 other fieldsHigh correlation
daily_vaccinations_raw is highly correlated with total_vaccinations and 5 other fieldsHigh correlation
daily_vaccinations is highly correlated with total_vaccinations and 5 other fieldsHigh correlation
total_vaccinations_per_hundred is highly correlated with people_vaccinated_per_hundred and 3 other fieldsHigh correlation
people_vaccinated_per_hundred is highly correlated with total_vaccinations_per_hundred and 3 other fieldsHigh correlation
people_fully_vaccinated_per_hundred is highly correlated with total_vaccinations_per_hundred and 3 other fieldsHigh correlation
vaccines is highly correlated with total_vaccinations and 8 other fieldsHigh correlation
source_name is highly correlated with total_vaccinations and 8 other fieldsHigh correlation
total_vaccinations has 20900 (45.5%) missing values Missing
people_vaccinated has 22038 (47.9%) missing values Missing
people_fully_vaccinated has 24935 (54.2%) missing values Missing
daily_vaccinations_raw has 25384 (55.2%) missing values Missing
total_vaccinations_per_hundred has 20900 (45.5%) missing values Missing
people_vaccinated_per_hundred has 22038 (47.9%) missing values Missing
people_fully_vaccinated_per_hundred has 24936 (54.2%) missing values Missing
people_fully_vaccinated is highly skewed (γ1 = 21.0654483) Skewed
people_fully_vaccinated_per_hundred has 475 (1.0%) zeros Zeros

Reproduction

Analysis started2021-10-30 23:03:55.362949
Analysis finished2021-10-30 23:04:17.083665
Duration21.72 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

country
Categorical

HIGH CARDINALITY

Distinct222
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size359.4 KiB
Denmark
 
293
Norway
 
292
Latvia
 
291
Scotland
 
286
England
 
286
Other values (217)
44536 

Length

Max length32
Median length7
Mean length8.732559151
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Denmark293
 
0.6%
Norway292
 
0.6%
Latvia291
 
0.6%
Scotland286
 
0.6%
England286
 
0.6%
Wales281
 
0.6%
Canada281
 
0.6%
United Kingdom281
 
0.6%
Northern Ireland281
 
0.6%
China280
 
0.6%
Other values (212)43132
93.8%

Length

2021-10-30T17:04:17.201351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and1790
 
3.0%
islands1199
 
2.0%
united815
 
1.4%
saint697
 
1.2%
new608
 
1.0%
south587
 
1.0%
ireland544
 
0.9%
guinea521
 
0.9%
northern495
 
0.8%
republic494
 
0.8%
Other values (249)51901
87.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

iso_code
Categorical

HIGH CARDINALITY

Distinct222
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size359.4 KiB
DNK
 
293
NOR
 
292
LVA
 
291
OWID_ENG
 
286
OWID_SCT
 
286
Other values (217)
44536 

Length

Max length8
Median length3
Mean length3.165818546
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAFG
2nd rowAFG
3rd rowAFG
4th rowAFG
5th rowAFG

Common Values

ValueCountFrequency (%)
DNK293
 
0.6%
NOR292
 
0.6%
LVA291
 
0.6%
OWID_ENG286
 
0.6%
OWID_SCT286
 
0.6%
GBR281
 
0.6%
CAN281
 
0.6%
OWID_WLS281
 
0.6%
OWID_NIR281
 
0.6%
RUS280
 
0.6%
Other values (212)43132
93.8%

Length

2021-10-30T17:04:17.347926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dnk293
 
0.6%
nor292
 
0.6%
lva291
 
0.6%
owid_eng286
 
0.6%
owid_sct286
 
0.6%
gbr281
 
0.6%
can281
 
0.6%
owid_wls281
 
0.6%
owid_nir281
 
0.6%
rus280
 
0.6%
Other values (212)43132
93.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

date
Categorical

HIGH CARDINALITY

Distinct294
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size359.4 KiB
02-08-2021
 
217
20-07-2021
 
217
10-08-2021
 
217
18-07-2021
 
217
27-07-2021
 
217
Other values (289)
44899 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row22-02-2021
2nd row23-02-2021
3rd row24-02-2021
4th row25-02-2021
5th row26-02-2021

Common Values

ValueCountFrequency (%)
02-08-2021217
 
0.5%
20-07-2021217
 
0.5%
10-08-2021217
 
0.5%
18-07-2021217
 
0.5%
27-07-2021217
 
0.5%
08-08-2021217
 
0.5%
29-06-2021217
 
0.5%
29-07-2021217
 
0.5%
28-06-2021217
 
0.5%
21-06-2021217
 
0.5%
Other values (284)43814
95.3%

Length

2021-10-30T17:04:17.477628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02-08-2021217
 
0.5%
19-07-2021217
 
0.5%
20-07-2021217
 
0.5%
15-08-2021217
 
0.5%
26-07-2021217
 
0.5%
24-06-2021217
 
0.5%
27-06-2021217
 
0.5%
14-08-2021217
 
0.5%
12-08-2021217
 
0.5%
22-06-2021217
 
0.5%
Other values (284)43814
95.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

total_vaccinations
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct24534
Distinct (%)97.8%
Missing20900
Missing (%)45.5%
Infinite0
Infinite (%)0.0%
Mean20487937.01
Minimum0
Maximum2180986000
Zeros139
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size359.4 KiB
2021-10-30T17:04:17.624189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13614.7
Q1207767.75
median1459863
Q37737194
95-th percentile68620795.95
Maximum2180986000
Range2180986000
Interquartile range (IQR)7529426.25

Descriptive statistics

Standard deviation117360316.8
Coefficient of variation (CV)5.728264236
Kurtosis196.3500563
Mean20487937.01
Median Absolute Deviation (MAD)1417767
Skewness13.05852627
Sum5.139194119 × 1011
Variance1.377344397 × 1016
MonotonicityNot monotonic
2021-10-30T17:04:17.791742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0139
 
0.3%
3920717
 
< 0.1%
4313911
 
< 0.1%
86558
 
< 0.1%
107087
 
< 0.1%
66
 
< 0.1%
385726
 
< 0.1%
25
 
< 0.1%
8415
 
< 0.1%
8465
 
< 0.1%
Other values (24524)24875
54.1%
(Missing)20900
45.5%
ValueCountFrequency (%)
0139
0.3%
13
 
< 0.1%
25
 
< 0.1%
32
 
< 0.1%
42
 
< 0.1%
52
 
< 0.1%
66
 
< 0.1%
72
 
< 0.1%
91
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
21809860001
< 0.1%
21776380001
< 0.1%
21740430001
< 0.1%
21700170001
< 0.1%
21656790001
< 0.1%
21614280001
< 0.1%
21569380001
< 0.1%
21525200001
< 0.1%
21481200001
< 0.1%
21425800001
< 0.1%

people_vaccinated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct23212
Distinct (%)96.9%
Missing22038
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean8757992.75
Minimum0
Maximum1100842000
Zeros129
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size359.4 KiB
2021-10-30T17:04:17.963313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11559
Q1166852.5
median998103.5
Q34679777.5
95-th percentile38529067.5
Maximum1100842000
Range1100842000
Interquartile range (IQR)4512925

Descriptive statistics

Standard deviation34301725.71
Coefficient of variation (CV)3.91661956
Kurtosis223.2831084
Mean8757992.75
Median Absolute Deviation (MAD)967123.5
Skewness12.08574061
Sum2.097188944 × 1011
Variance1.176608387 × 1015
MonotonicityNot monotonic
2021-10-30T17:04:18.121891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0129
 
0.3%
582324528
 
0.1%
211308013
 
< 0.1%
2477310
 
< 0.1%
48624210
 
< 0.1%
9281078
 
< 0.1%
86558
 
< 0.1%
107087
 
< 0.1%
1421257
 
< 0.1%
26118076
 
< 0.1%
Other values (23202)23720
51.6%
(Missing)22038
47.9%
ValueCountFrequency (%)
0129
0.3%
13
 
< 0.1%
25
 
< 0.1%
32
 
< 0.1%
42
 
< 0.1%
52
 
< 0.1%
66
 
< 0.1%
72
 
< 0.1%
91
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
11008420001
< 0.1%
10950000001
< 0.1%
10725000001
< 0.1%
6220000001
< 0.1%
6065632371
< 0.1%
6022067891
< 0.1%
5972052961
< 0.1%
5861052331
< 0.1%
5828421081
< 0.1%
5768562631
< 0.1%

people_fully_vaccinated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct20122
Distinct (%)95.6%
Missing24935
Missing (%)54.2%
Infinite0
Infinite (%)0.0%
Mean5583140.034
Minimum1
Maximum1022207000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size359.4 KiB
2021-10-30T17:04:18.293435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3451.2
Q180363
median635314
Q33270303
95-th percentile27643648.2
Maximum1022207000
Range1022206999
Interquartile range (IQR)3189940

Descriptive statistics

Standard deviation22116692.76
Coefficient of variation (CV)3.961335848
Kurtosis795.5951026
Mean5583140.034
Median Absolute Deviation (MAD)620003
Skewness21.0654483
Sum1.175195146 × 1011
Variance4.891480985 × 1014
MonotonicityNot monotonic
2021-10-30T17:04:18.476944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169
 
0.2%
233
 
0.1%
531
 
0.1%
117927
 
0.1%
620
 
< 0.1%
1901920
 
< 0.1%
318
 
< 0.1%
717
 
< 0.1%
816
 
< 0.1%
34114
 
< 0.1%
Other values (20112)20784
45.2%
(Missing)24935
54.2%
ValueCountFrequency (%)
169
0.2%
233
0.1%
318
 
< 0.1%
410
 
< 0.1%
531
0.1%
620
 
< 0.1%
717
 
< 0.1%
816
 
< 0.1%
98
 
< 0.1%
108
 
< 0.1%
ValueCountFrequency (%)
10222070001
< 0.1%
10115840001
< 0.1%
9697200001
< 0.1%
8894390001
< 0.1%
7770460001
< 0.1%
2056692791
< 0.1%
2025719071
< 0.1%
1988098231
< 0.1%
1912826171
< 0.1%
1884995331
< 0.1%

daily_vaccinations_raw
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct17407
Distinct (%)84.5%
Missing25384
Missing (%)55.2%
Infinite0
Infinite (%)0.0%
Mean259785.02
Minimum0
Maximum24741000
Zeros240
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size359.4 KiB
2021-10-30T17:04:18.658454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile297
Q14898
median24106.5
Q3111688.75
95-th percentile734813.45
Maximum24741000
Range24741000
Interquartile range (IQR)106790.75

Descriptive statistics

Standard deviation1317280.917
Coefficient of variation (CV)5.070657717
Kurtosis142.5510671
Mean259785.02
Median Absolute Deviation (MAD)23000.5
Skewness11.10058644
Sum5351571413
Variance1.735229013 × 1012
MonotonicityNot monotonic
2021-10-30T17:04:18.837976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0240
 
0.5%
137
 
0.1%
314
 
< 0.1%
413
 
< 0.1%
511
 
< 0.1%
210
 
< 0.1%
309
 
< 0.1%
79
 
< 0.1%
10258
 
< 0.1%
88
 
< 0.1%
Other values (17397)20241
44.0%
(Missing)25384
55.2%
ValueCountFrequency (%)
0240
0.5%
137
 
0.1%
210
 
< 0.1%
314
 
< 0.1%
413
 
< 0.1%
511
 
< 0.1%
64
 
< 0.1%
79
 
< 0.1%
88
 
< 0.1%
95
 
< 0.1%
ValueCountFrequency (%)
247410001
< 0.1%
241190001
< 0.1%
236050001
< 0.1%
231620001
< 0.1%
229180001
< 0.1%
222960001
< 0.1%
220390001
< 0.1%
215020001
< 0.1%
214250001
< 0.1%
212400001
< 0.1%

daily_vaccinations
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct24529
Distinct (%)53.7%
Missing307
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean130853.5448
Minimum0
Maximum22424286
Zeros204
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size359.4 KiB
2021-10-30T17:04:19.018492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75
Q1871
median6774
Q341695
95-th percentile419247
Maximum22424286
Range22424286
Interquartile range (IQR)40824

Descriptive statistics

Standard deviation875853.8116
Coefficient of variation (CV)6.693390024
Kurtosis304.4140463
Mean130853.5448
Median Absolute Deviation (MAD)6598
Skewness16.13645739
Sum5976997368
Variance7.671198993 × 1011
MonotonicityNot monotonic
2021-10-30T17:04:19.173078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0204
 
0.4%
22369176
 
0.4%
196158
 
0.3%
1111
 
0.2%
31288
 
0.2%
20280
 
0.2%
5780
 
0.2%
42578
 
0.2%
477
 
0.2%
274
 
0.2%
Other values (24519)44551
96.9%
(Missing)307
 
0.7%
ValueCountFrequency (%)
0204
0.4%
1111
0.2%
274
 
0.2%
329
 
0.1%
477
 
0.2%
527
 
0.1%
633
 
0.1%
730
 
0.1%
818
 
< 0.1%
917
 
< 0.1%
ValueCountFrequency (%)
224242861
< 0.1%
223662861
< 0.1%
221058571
< 0.1%
219987141
< 0.1%
219930001
< 0.1%
219354291
< 0.1%
215360001
< 0.1%
212532861
< 0.1%
211247141
< 0.1%
208014291
< 0.1%

total_vaccinations_per_hundred
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct10351
Distinct (%)41.3%
Missing20900
Missing (%)45.5%
Infinite0
Infinite (%)0.0%
Mean43.74065859
Minimum0
Maximum235.39
Zeros245
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size359.4 KiB
2021-10-30T17:04:19.336642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29
Q15.27
median26.44
Q373.4425
95-th percentile132.6285
Maximum235.39
Range235.39
Interquartile range (IQR)68.1725

Descriptive statistics

Standard deviation45.0444271
Coefficient of variation (CV)1.029806787
Kurtosis0.116460321
Mean43.74065859
Median Absolute Deviation (MAD)24.475
Skewness1.002541199
Sum1097190.68
Variance2029.000413
MonotonicityNot monotonic
2021-10-30T17:04:19.515164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0245
 
0.5%
0.0195
 
0.2%
0.0270
 
0.2%
0.0657
 
0.1%
0.0755
 
0.1%
0.0454
 
0.1%
0.0553
 
0.1%
0.0350
 
0.1%
0.137
 
0.1%
0.1237
 
0.1%
Other values (10341)24331
52.9%
(Missing)20900
45.5%
ValueCountFrequency (%)
0245
0.5%
0.0195
 
0.2%
0.0270
 
0.2%
0.0350
 
0.1%
0.0454
 
0.1%
0.0553
 
0.1%
0.0657
 
0.1%
0.0755
 
0.1%
0.0830
 
0.1%
0.0934
 
0.1%
ValueCountFrequency (%)
235.391
< 0.1%
235.281
< 0.1%
235.181
< 0.1%
235.051
< 0.1%
234.831
< 0.1%
234.71
< 0.1%
234.561
< 0.1%
234.531
< 0.1%
234.371
< 0.1%
234.151
< 0.1%

people_vaccinated_per_hundred
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7071
Distinct (%)29.5%
Missing22038
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean26.56949804
Minimum0
Maximum118.27
Zeros228
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size359.4 KiB
2021-10-30T17:04:19.675702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28
Q14.1125
median18.6
Q346.3
95-th percentile71.1
Maximum118.27
Range118.27
Interquartile range (IQR)42.1875

Descriptive statistics

Standard deviation24.458658
Coefficient of variation (CV)0.9205540114
Kurtosis-0.6370711252
Mean26.56949804
Median Absolute Deviation (MAD)16.65
Skewness0.6922236367
Sum636233.2
Variance598.2259511
MonotonicityNot monotonic
2021-10-30T17:04:19.839267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0228
 
0.5%
0.0193
 
0.2%
0.0269
 
0.2%
0.0764
 
0.1%
3.562
 
0.1%
0.0453
 
0.1%
0.0552
 
0.1%
0.0651
 
0.1%
0.0348
 
0.1%
0.2439
 
0.1%
Other values (7061)23187
50.4%
(Missing)22038
47.9%
ValueCountFrequency (%)
0228
0.5%
0.0193
0.2%
0.0269
 
0.2%
0.0348
 
0.1%
0.0453
 
0.1%
0.0552
 
0.1%
0.0651
 
0.1%
0.0764
 
0.1%
0.0822
 
< 0.1%
0.0936
 
0.1%
ValueCountFrequency (%)
118.271
< 0.1%
118.241
< 0.1%
118.21
< 0.1%
118.161
< 0.1%
118.091
< 0.1%
118.051
< 0.1%
1182
< 0.1%
117.921
< 0.1%
117.711
< 0.1%
117.621
< 0.1%

people_fully_vaccinated_per_hundred
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct6043
Distinct (%)28.7%
Missing24936
Missing (%)54.2%
Infinite0
Infinite (%)0.0%
Mean19.68974249
Minimum0
Maximum117.12
Zeros475
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size359.4 KiB
2021-10-30T17:04:20.015830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07
Q12.3
median10.645
Q332.2525
95-th percentile62.9065
Maximum117.12
Range117.12
Interquartile range (IQR)29.9525

Descriptive statistics

Standard deviation21.46713526
Coefficient of variation (CV)1.090269985
Kurtosis0.5469886012
Mean19.68974249
Median Absolute Deviation (MAD)9.915
Skewness1.14982895
Sum414429.7
Variance460.8378962
MonotonicityNot monotonic
2021-10-30T17:04:20.175408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0475
 
1.0%
0.01178
 
0.4%
0.0299
 
0.2%
0.0887
 
0.2%
0.0484
 
0.2%
0.0383
 
0.2%
0.0764
 
0.1%
0.0564
 
0.1%
0.0658
 
0.1%
0.2957
 
0.1%
Other values (6033)19799
43.1%
(Missing)24936
54.2%
ValueCountFrequency (%)
0475
1.0%
0.01178
 
0.4%
0.0299
 
0.2%
0.0383
 
0.2%
0.0484
 
0.2%
0.0564
 
0.1%
0.0658
 
0.1%
0.0764
 
0.1%
0.0887
 
0.2%
0.0941
 
0.1%
ValueCountFrequency (%)
117.121
< 0.1%
117.041
< 0.1%
116.971
< 0.1%
116.891
< 0.1%
116.741
< 0.1%
116.651
< 0.1%
116.561
< 0.1%
116.521
< 0.1%
116.451
< 0.1%
116.441
< 0.1%

daily_vaccinations_per_million
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct10997
Distinct (%)24.1%
Missing307
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean3547.60352
Minimum0
Maximum117497
Zeros255
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size359.4 KiB
2021-10-30T17:04:20.342960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43
Q1505
median2191
Q35307
95-th percentile10735.4
Maximum117497
Range117497
Interquartile range (IQR)4802

Descriptive statistics

Standard deviation4429.261509
Coefficient of variation (CV)1.248522132
Kurtosis66.83767559
Mean3547.60352
Median Absolute Deviation (MAD)1925
Skewness4.861220497
Sum162043886
Variance19618357.52
MonotonicityNot monotonic
2021-10-30T17:04:20.504807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0255
 
0.6%
501184
 
0.4%
7412148
 
0.3%
176145
 
0.3%
43141
 
0.3%
4102
 
0.2%
2895
 
0.2%
384
 
0.2%
1584
 
0.2%
795478
 
0.2%
Other values (10987)44361
96.5%
(Missing)307
 
0.7%
ValueCountFrequency (%)
0255
0.6%
165
 
0.1%
267
 
0.1%
384
 
0.2%
4102
 
0.2%
565
 
0.1%
625
 
0.1%
767
 
0.1%
869
 
0.2%
958
 
0.1%
ValueCountFrequency (%)
1174971
< 0.1%
1174101
< 0.1%
1102051
< 0.1%
1092821
< 0.1%
1012351
< 0.1%
929261
< 0.1%
830861
< 0.1%
786041
< 0.1%
738541
< 0.1%
728681
< 0.1%

vaccines
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct68
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size359.4 KiB
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech
5952 
Oxford/AstraZeneca
5565 
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech
3742 
Oxford/AstraZeneca, Sinopharm/Beijing
3038 
Oxford/AstraZeneca, Pfizer/BioNTech
3015 
Other values (63)
24672 

Length

Max length101
Median length44
Mean length44.43786969
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowJohnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing
2nd rowJohnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing
3rd rowJohnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing
4th rowJohnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing
5th rowJohnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing

Common Values

ValueCountFrequency (%)
Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech5952
 
12.9%
Oxford/AstraZeneca5565
 
12.1%
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech3742
 
8.1%
Oxford/AstraZeneca, Sinopharm/Beijing3038
 
6.6%
Oxford/AstraZeneca, Pfizer/BioNTech3015
 
6.6%
Moderna, Pfizer/BioNTech1939
 
4.2%
Pfizer/BioNTech1724
 
3.7%
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V1618
 
3.5%
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing887
 
1.9%
Oxford/AstraZeneca, Sputnik V822
 
1.8%
Other values (58)17682
38.5%

Length

2021-10-30T17:04:20.711257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oxford/astrazeneca37538
25.6%
pfizer/biontech29892
20.4%
moderna17354
11.8%
sinopharm/beijing14024
 
9.6%
johnson&johnson11396
 
7.8%
sputnik10811
 
7.4%
v10811
 
7.4%
sinovac8661
 
5.9%
cansino1768
 
1.2%
covaxin1410
 
1.0%
Other values (10)2818
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

source_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct86
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size359.4 KiB
Ministry of Health
10669 
World Health Organization
9926 
SPC Public Health Division
 
1917
Pan American Health Organization
 
1582
Government of the United Kingdom
 
1415
Other values (81)
20475 

Length

Max length70
Median length25
Mean length26.8049104
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWorld Health Organization
2nd rowWorld Health Organization
3rd rowWorld Health Organization
4th rowWorld Health Organization
5th rowWorld Health Organization

Common Values

ValueCountFrequency (%)
Ministry of Health10669
23.2%
World Health Organization9926
21.6%
SPC Public Health Division1917
 
4.2%
Pan American Health Organization1582
 
3.4%
Government of the United Kingdom1415
 
3.1%
Africa Centres for Disease Control and Prevention1271
 
2.8%
Federal Office of Public Health544
 
1.2%
Ministry of Public Health488
 
1.1%
Statens Serum Institute293
 
0.6%
Norwegian Institute of Public Health292
 
0.6%
Other values (76)17587
38.2%

Length

2021-10-30T17:04:21.759452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
health30246
17.6%
of23316
 
13.5%
ministry12013
 
7.0%
organization11508
 
6.7%
world9926
 
5.8%
government8997
 
5.2%
public4886
 
2.8%
for3177
 
1.8%
and3101
 
1.8%
via2916
 
1.7%
Other values (146)62223
36.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

source_website
Categorical

HIGH CARDINALITY

Distinct146
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size359.4 KiB
https://covid19.who.int/
7530 
https://stats.pacificdata.org/vis?tm=covid&pg=0&df[ds]=SPC2&df[id]=DF_COVID_VACCINATION&df[ag]=SPC&df[vs]=1.0
 
2020
https://africacdc.org/covid-19-vaccination/
 
1719
https://ais.paho.org/imm/IM_DosisAdmin-Vacunacion.asp
 
1519
https://coronavirus.data.gov.uk/details/vaccinations
 
1415
Other values (141)
31781 

Length

Max length170
Median length51
Mean length56.42473469
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
2nd rowhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
3rd rowhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
4th rowhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
5th rowhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9

Common Values

ValueCountFrequency (%)
https://covid19.who.int/7530
 
16.4%
https://stats.pacificdata.org/vis?tm=covid&pg=0&df[ds]=SPC2&df[id]=DF_COVID_VACCINATION&df[ag]=SPC&df[vs]=1.02020
 
4.4%
https://africacdc.org/covid-19-vaccination/1719
 
3.7%
https://ais.paho.org/imm/IM_DosisAdmin-Vacunacion.asp1519
 
3.3%
https://coronavirus.data.gov.uk/details/vaccinations1415
 
3.1%
https://who.maps.arcgis.com/apps/dashboards/ead3c6475654481ca51c248d52ab9c61476
 
1.0%
https://www.who.int/southeastasia/health-topics/immunization/covid-19-vaccination398
 
0.9%
https://covid19.ssi.dk/overvagningsdata/download-fil-med-vaccinationsdata293
 
0.6%
https://www.fhi.no/sv/vaksine/koronavaksinasjonsprogrammet/koronavaksinasjonsstatistikk/292
 
0.6%
https://data.gov.lv/dati/eng/dataset/covid19-vakcinacijas291
 
0.6%
Other values (136)30031
65.3%

Length

2021-10-30T17:04:21.956924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://covid19.who.int7573
 
16.5%
https://stats.pacificdata.org/vis?tm=covid&pg=0&df[ds]=spc2&df[id]=df_covid_vaccination&df[ag]=spc&df[vs]=1.02020
 
4.4%
https://africacdc.org/covid-19-vaccination1719
 
3.7%
https://ais.paho.org/imm/im_dosisadmin-vacunacion.asp1519
 
3.3%
https://coronavirus.data.gov.uk/details/vaccinations1415
 
3.1%
https://who.maps.arcgis.com/apps/dashboards/ead3c6475654481ca51c248d52ab9c61476
 
1.0%
https://www.who.int/southeastasia/health-topics/immunization/covid-19-vaccination398
 
0.9%
https://covid19.ssi.dk/overvagningsdata/download-fil-med-vaccinationsdata293
 
0.6%
https://www.fhi.no/sv/vaksine/koronavaksinasjonsprogrammet/koronavaksinasjonsstatistikk292
 
0.6%
https://data.gov.lv/dati/eng/dataset/covid19-vakcinacijas291
 
0.6%
Other values (135)29988
65.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-30T17:04:13.756530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:00.314505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:01.830451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:03.450121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:05.076775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:06.702427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:08.311090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:10.114305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:11.911465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:13.930100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:00.478038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:01.997008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:03.621631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:05.241299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:06.861965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:08.545464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:10.283817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:12.143844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:14.107623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:00.649576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:02.169515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:03.793204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:05.431822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:07.053454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:08.765879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:10.525203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:12.327387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:14.304097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:00.819124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:02.352060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:03.964714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:05.615332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:07.230013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:08.990276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:10.698738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:12.559772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:14.490599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:00.985710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:02.555482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:04.160191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:05.789832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:07.402551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:09.158856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:10.891227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:12.752250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:14.663133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:01.146278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:02.750990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:04.346691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:05.955390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:07.565119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:09.356296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:11.119581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:13.011522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:14.828664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:01.302864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:02.915550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:04.510255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:06.120978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:07.723697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:09.535816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:11.304120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:13.183064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:15.006188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:01.473376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:03.092079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:04.697754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:06.314431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:07.907207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:09.703369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:11.479619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:13.361587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:15.203660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:01.654888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:03.267579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:04.876307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:06.507914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:08.091711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:09.938740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:11.669113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T17:04:13.556072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-30T17:04:22.118490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-30T17:04:22.401773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-30T17:04:22.697942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-30T17:04:22.989164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-30T17:04:23.139761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-30T17:04:15.511868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-30T17:04:15.994548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-30T17:04:16.449331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-30T17:04:16.841281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

countryiso_codedatetotal_vaccinationspeople_vaccinatedpeople_fully_vaccinateddaily_vaccinations_rawdaily_vaccinationstotal_vaccinations_per_hundredpeople_vaccinated_per_hundredpeople_fully_vaccinated_per_hundreddaily_vaccinations_per_millionvaccinessource_namesource_website
0AfghanistanAFG22-02-20210.00.0NaNNaNNaN0.000.00NaNNaNJohnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
1AfghanistanAFG23-02-2021NaNNaNNaNNaN1367.0NaNNaNNaN34.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
2AfghanistanAFG24-02-2021NaNNaNNaNNaN1367.0NaNNaNNaN34.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
3AfghanistanAFG25-02-2021NaNNaNNaNNaN1367.0NaNNaNNaN34.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
4AfghanistanAFG26-02-2021NaNNaNNaNNaN1367.0NaNNaNNaN34.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
5AfghanistanAFG27-02-2021NaNNaNNaNNaN1367.0NaNNaNNaN34.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
6AfghanistanAFG28-02-20218200.08200.0NaNNaN1367.00.020.02NaN34.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
7AfghanistanAFG01-03-2021NaNNaNNaNNaN1580.0NaNNaNNaN40.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
8AfghanistanAFG02-03-2021NaNNaNNaNNaN1794.0NaNNaNNaN45.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9
9AfghanistanAFG03-03-2021NaNNaNNaNNaN2008.0NaNNaNNaN50.0Johnson&Johnson, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/BeijingWorld Health Organizationhttps://app.powerbi.com/view?r=eyJrIjoiYTkyM2VmMDUtNDJjOC00YjU2LWI3Y2MtNTRhMWY4YzU0YTRlIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9

Last rows

countryiso_codedatetotal_vaccinationspeople_vaccinatedpeople_fully_vaccinateddaily_vaccinations_rawdaily_vaccinationstotal_vaccinations_per_hundredpeople_vaccinated_per_hundredpeople_fully_vaccinated_per_hundreddaily_vaccinations_per_millionvaccinessource_namesource_website
45974ZimbabweZWE11-09-20214708905.02844848.01864057.052457.044094.031.2018.8512.352922.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
45975ZimbabweZWE12-09-2021NaNNaNNaNNaN42719.0NaNNaNNaN2831.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
45976ZimbabweZWE13-09-20214752356.02856655.01895701.0NaN41369.031.4918.9312.562741.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
45977ZimbabweZWE14-09-20214800761.02873593.01927168.048405.043831.031.8119.0412.772904.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
45978ZimbabweZWE15-09-20214855816.02891837.01963979.055055.043249.032.1719.1613.012866.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
45979ZimbabweZWE16-09-20214964302.02930550.02033752.0108486.051755.032.8919.4213.483429.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
45980ZimbabweZWE17-09-2021NaNNaNNaNNaN45993.0NaNNaNNaN3047.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
45981ZimbabweZWE18-09-20214992501.02940750.02051751.0NaN40514.033.0819.4913.592684.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
45982ZimbabweZWE19-09-20215015041.02948725.02066316.022540.040630.033.2319.5413.692692.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd
45983ZimbabweZWE20-09-20215044809.02961845.02082964.029768.041779.033.4319.6313.802768.0Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac, Sputnik VMinistry of Healthhttps://www.arcgis.com/home/webmap/viewer.html?url=https://services9.arcgis.com/DnERH4rcjw7NU6lv/ArcGIS/rest/services/Vaccine_Distribution_Program/FeatureServer&source=sd